Pandas Profiling Report

Dataset statistics

Number of variables	12
Number of observations	714
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	67.1 KiB
Average record size in memory	96.2 B

Variable types

Numeric	6
Categorical	6

Alerts

`Name` has a high cardinality: 714 distinct values	High cardinality
`Ticket` has a high cardinality: 542 distinct values	High cardinality
`df_index` is highly correlated with `PassengerId`	High correlation
`PassengerId` is highly correlated with `df_index`	High correlation
`Pclass` is highly correlated with `Fare`	High correlation
`Fare` is highly correlated with `Pclass`	High correlation
`df_index` is highly correlated with `PassengerId`	High correlation
`PassengerId` is highly correlated with `df_index`	High correlation
`Pclass` is highly correlated with `Fare`	High correlation
`Fare` is highly correlated with `Pclass`	High correlation
`df_index` is highly correlated with `PassengerId`	High correlation
`PassengerId` is highly correlated with `df_index`	High correlation
`Pclass` is highly correlated with `Fare`	High correlation
`Fare` is highly correlated with `Pclass`	High correlation
`Sex` is highly correlated with `Survived`	High correlation
`Survived` is highly correlated with `Sex`	High correlation
`df_index` is highly correlated with `PassengerId`	High correlation
`PassengerId` is highly correlated with `df_index`	High correlation
`Survived` is highly correlated with `Sex`	High correlation
`Pclass` is highly correlated with `Fare` and 1 other fields	High correlation
`Sex` is highly correlated with `Survived`	High correlation
`Fare` is highly correlated with `Pclass`	High correlation
`Embark` is highly correlated with `Pclass`	High correlation
`df_index` is uniformly distributed	Uniform
`PassengerId` is uniformly distributed	Uniform
`Name` is uniformly distributed	Uniform
`Ticket` is uniformly distributed	Uniform
`df_index` has unique values	Unique
`PassengerId` has unique values	Unique
`Name` has unique values	Unique
`SibSp` has 471 (66.0%) zeros	Zeros
`Parch` has 521 (73.0%) zeros	Zeros

Reproduction

Analysis started	2022-10-09 08:01:16.724608
Analysis finished	2022-10-09 08:01:23.340626
Duration	6.62 seconds
Software version	pandas-profiling v3.2.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	714
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	447.5826331

Minimum	0
Maximum	890
Zeros	1
Zeros (%)	0.1%
Negative	0
Negative (%)	0.0%
Memory size	5.7 KiB

Quantile statistics

Minimum	0
5-th percentile	49.65
Q1	221.25
median	444
Q3	676.75
95-th percentile	848.7
Maximum	890
Range	890
Interquartile range (IQR)	455.5

Descriptive statistics

Standard deviation	259.1195244
Coefficient of variation (CV)	0.578931141
Kurtosis	-1.224109035
Mean	447.5826331
Median Absolute Deviation (MAD)	227.5
Skewness	-0.0006094557038
Sum	319574
Variance	67142.92794
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1	0.1%
621	1	0.1%
594	1	0.1%
595	1	0.1%
597	1	0.1%
599	1	0.1%
600	1	0.1%
603	1	0.1%
604	1	0.1%
605	1	0.1%
Other values (704)	704	98.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	1	0.1%
1	1	0.1%
2	1	0.1%
3	1	0.1%
4	1	0.1%
6	1	0.1%
7	1	0.1%
8	1	0.1%
9	1	0.1%
10	1	0.1%

Value	Count	Frequency (%)
890	1	0.1%
889	1	0.1%
887	1	0.1%
886	1	0.1%
885	1	0.1%
884	1	0.1%
883	1	0.1%
882	1	0.1%
881	1	0.1%
880	1	0.1%

PassengerId
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	714
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	448.5826331

Minimum	1
Maximum	891
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.7 KiB

Quantile statistics

Minimum	1
5-th percentile	50.65
Q1	222.25
median	445
Q3	677.75
95-th percentile	849.7
Maximum	891
Range	890
Interquartile range (IQR)	455.5

Descriptive statistics

Standard deviation	259.1195244
Coefficient of variation (CV)	0.5776405624
Kurtosis	-1.224109035
Mean	448.5826331
Median Absolute Deviation (MAD)	227.5
Skewness	-0.0006094557038
Sum	320288
Variance	67142.92794
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.1%
622	1	0.1%
595	1	0.1%
596	1	0.1%
598	1	0.1%
600	1	0.1%
601	1	0.1%
604	1	0.1%
605	1	0.1%
606	1	0.1%
Other values (704)	704	98.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.1%
2	1	0.1%
3	1	0.1%
4	1	0.1%
5	1	0.1%
7	1	0.1%
8	1	0.1%
9	1	0.1%
10	1	0.1%
11	1	0.1%

Value	Count	Frequency (%)
891	1	0.1%
890	1	0.1%
888	1	0.1%
887	1	0.1%
886	1	0.1%
885	1	0.1%
884	1	0.1%
883	1	0.1%
882	1	0.1%
881	1	0.1%

Survived
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	5.7 KiB

0	424
1	290

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	714
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	1
3rd row	1
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
0	424	59.4%
1	290	40.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
0	424	59.4%
1	290	40.6%

Most occurring characters

Value	Count	Frequency (%)
0	424	59.4%
1	290	40.6%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	714	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	424	59.4%
1	290	40.6%

Most occurring scripts

Value	Count	Frequency (%)
Common	714	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	424	59.4%
1	290	40.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	714	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	424	59.4%
1	290	40.6%

Pclass
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	5.7 KiB

3	355
1	186
2	173

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	714
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	3
2nd row	1
3rd row	3
4th row	1
5th row	3

Common Values

Value	Count	Frequency (%)
3	355	49.7%
1	186	26.1%
2	173	24.2%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
3	355	49.7%
1	186	26.1%
2	173	24.2%

Most occurring characters

Value	Count	Frequency (%)
3	355	49.7%
1	186	26.1%
2	173	24.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	714	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
3	355	49.7%
1	186	26.1%
2	173	24.2%

Most occurring scripts

Value	Count	Frequency (%)
Common	714	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
3	355	49.7%
1	186	26.1%
2	173	24.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	714	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	355	49.7%
1	186	26.1%
2	173	24.2%

Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	714
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	5.7 KiB

Braund, Mr. Owen Harris	1
Kimball, Mr. Edwin Nelson Jr	1
Chapman, Mr. John Henry	1
Van Impe, Mr. Jean Baptiste	1
Johnson, Mr. Alfred	1
Other values (709)	709

Length

Max length	82
Median length	52
Mean length	27.69327731
Min length	13

Characters and Unicode

Total characters	19773
Distinct characters	59
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	714 ?
Unique (%)	100.0%

Sample

1st row	Braund, Mr. Owen Harris
2nd row	Cumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd row	Heikkinen, Miss. Laina
4th row	Futrelle, Mrs. Jacques Heath (Lily May Peel)
5th row	Allen, Mr. William Henry

Common Values

Value	Count	Frequency (%)
Braund, Mr. Owen Harris	1	0.1%
Kimball, Mr. Edwin Nelson Jr	1	0.1%
Chapman, Mr. John Henry	1	0.1%
Van Impe, Mr. Jean Baptiste	1	0.1%
Johnson, Mr. Alfred	1	0.1%
Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan")	1	0.1%
Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy)	1	0.1%
Torber, Mr. Ernst William	1	0.1%
Homer, Mr. Harry ("Mr E Haven")	1	0.1%
Lindell, Mr. Edvard Bengtsson	1	0.1%
Other values (704)	704	98.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
mr	402	13.5%
miss	146	4.9%
mrs	112	3.8%
william	55	1.9%
john	36	1.2%
master	36	1.2%
henry	28	0.9%
charles	19	0.6%
james	18	0.6%
george	18	0.6%
Other values (1297)	2097	70.7%

Most occurring characters

Value	Count	Frequency (%)
	2255	11.4%
r	1591	8.0%
e	1390	7.0%
a	1375	7.0%
i	1113	5.6%
s	1095	5.5%
n	1090	5.5%
l	914	4.6%
M	884	4.5%
o	818	4.1%
Other values (49)	7248	36.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	12785	64.7%
Uppercase Letter	2972	15.0%
Space Separator	2255	11.4%
Other Punctuation	1500	7.6%
Open Punctuation	125	0.6%
Close Punctuation	125	0.6%
Dash Punctuation	11	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
r	1591	12.4%
e	1390	10.9%
a	1375	10.8%
i	1113	8.7%
s	1095	8.6%
n	1090	8.5%
l	914	7.1%
o	818	6.4%
t	557	4.4%
d	423	3.3%
Other values (16)	2419	18.9%

Uppercase Letter

Value	Count	Frequency (%)
M	884	29.7%
A	222	7.5%
J	176	5.9%
H	169	5.7%
E	149	5.0%
C	147	4.9%
S	145	4.9%
W	120	4.0%
B	119	4.0%
L	110	3.7%
Other values (15)	731	24.6%

Other Punctuation

Value	Count	Frequency (%)
.	715	47.7%
,	714	47.6%
"	70	4.7%
/	1	0.1%

Space Separator

Value	Count	Frequency (%)
	2255	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	125	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	125	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	11	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	15757	79.7%
Common	4016	20.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
r	1591	10.1%
e	1390	8.8%
a	1375	8.7%
i	1113	7.1%
s	1095	6.9%
n	1090	6.9%
l	914	5.8%
M	884	5.6%
o	818	5.2%
t	557	3.5%
Other values (41)	4930	31.3%

Common

Value	Count	Frequency (%)
	2255	56.2%
.	715	17.8%
,	714	17.8%
(	125	3.1%
)	125	3.1%
"	70	1.7%
-	11	0.3%
/	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	19773	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	2255	11.4%
r	1591	8.0%
e	1390	7.0%
a	1375	7.0%
i	1113	5.6%
s	1095	5.5%
n	1090	5.5%
l	914	4.6%
M	884	4.5%
o	818	4.1%
Other values (49)	7248	36.7%

Sex
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	5.7 KiB

male	453
female	261

Length

Max length	6
Median length	4
Mean length	4.731092437
Min length	4

Characters and Unicode

Total characters	3378
Distinct characters	5
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	male
2nd row	female
3rd row	female
4th row	female
5th row	male

Common Values

Value	Count	Frequency (%)
male	453	63.4%
female	261	36.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
male	453	63.4%
female	261	36.6%

Most occurring characters

Value	Count	Frequency (%)
e	975	28.9%
m	714	21.1%
a	714	21.1%
l	714	21.1%
f	261	7.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	3378	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	975	28.9%
m	714	21.1%
a	714	21.1%
l	714	21.1%
f	261	7.7%

Most occurring scripts

Value	Count	Frequency (%)
Latin	3378	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	975	28.9%
m	714	21.1%
a	714	21.1%
l	714	21.1%
f	261	7.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	3378	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	975	28.9%
m	714	21.1%
a	714	21.1%
l	714	21.1%
f	261	7.7%

Age
Real number (ℝ_≥0)

Distinct	88
Distinct (%)	12.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	29.69911765

Minimum	0.42
Maximum	80
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	5.7 KiB

Quantile statistics

Minimum	0.42
5-th percentile	4
Q1	20.125
median	28
Q3	38
95-th percentile	56
Maximum	80
Range	79.58
Interquartile range (IQR)	17.875

Descriptive statistics

Standard deviation	14.52649733
Coefficient of variation (CV)	0.4891221855
Kurtosis	0.1782741536
Mean	29.69911765
Median Absolute Deviation (MAD)	9
Skewness	0.3891077823
Sum	21205.17
Variance	211.0191247
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
24	30	4.2%
22	27	3.8%
18	26	3.6%
19	25	3.5%
28	25	3.5%
30	25	3.5%
21	24	3.4%
25	23	3.2%
36	22	3.1%
29	20	2.8%
Other values (78)	467	65.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.42	1	0.1%
0.67	1	0.1%
0.75	2	0.3%
0.83	2	0.3%
0.92	1	0.1%
1	7	1.0%
2	10	1.4%
3	6	0.8%
4	10	1.4%
5	4	0.6%

Value	Count	Frequency (%)
80	1	0.1%
74	1	0.1%
71	2	0.3%
70.5	1	0.1%
70	2	0.3%
66	1	0.1%
65	3	0.4%
64	2	0.3%
63	2	0.3%
62	4	0.6%

SibSp
Real number (ℝ_≥0)

ZEROS

Distinct	6
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.512605042

Minimum	0
Maximum	5
Zeros	471
Zeros (%)	66.0%
Negative	0
Negative (%)	0.0%
Memory size	5.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	2
Maximum	5
Range	5
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.9297834541
Coefficient of variation (CV)	1.813839853
Kurtosis	7.044950785
Mean	0.512605042
Median Absolute Deviation (MAD)	0
Skewness	2.519576762
Sum	366
Variance	0.8644972716
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	471	66.0%
1	183	25.6%
2	25	3.5%
4	18	2.5%
3	12	1.7%
5	5	0.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	471	66.0%
1	183	25.6%
2	25	3.5%
3	12	1.7%
4	18	2.5%
5	5	0.7%

Value	Count	Frequency (%)
5	5	0.7%
4	18	2.5%
3	12	1.7%
2	25	3.5%
1	183	25.6%
0	471	66.0%

Parch
Real number (ℝ_≥0)

ZEROS

Distinct	7
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.431372549

Minimum	0
Maximum	6
Zeros	521
Zeros (%)	73.0%
Negative	0
Negative (%)	0.0%
Memory size	5.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	1
95-th percentile	2
Maximum	6
Range	6
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.8532893658
Coefficient of variation (CV)	1.978079893
Kurtosis	8.853125533
Mean	0.431372549
Median Absolute Deviation (MAD)	0
Skewness	2.618913989
Sum	308
Variance	0.7281027418
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	521	73.0%
1	110	15.4%
2	68	9.5%
5	5	0.7%
3	5	0.7%
4	4	0.6%
6	1	0.1%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	521	73.0%
1	110	15.4%
2	68	9.5%
3	5	0.7%
4	4	0.6%
5	5	0.7%
6	1	0.1%

Value	Count	Frequency (%)
6	1	0.1%
5	5	0.7%
4	4	0.6%
3	5	0.7%
2	68	9.5%
1	110	15.4%
0	521	73.0%

Ticket
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	542
Distinct (%)	75.9%
Missing	0
Missing (%)	0.0%
Memory size	5.7 KiB

347082	7
3101295	6
CA 2144	6
347088	6
382652	5
Other values (537)	684

Length

Max length	18
Median length	17
Mean length	6.841736695
Min length	3

Characters and Unicode

Total characters	4885
Distinct characters	35
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	431 ?
Unique (%)	60.4%

Sample

1st row	A/5 21171
2nd row	PC 17599
3rd row	STON/O2. 3101282
4th row	113803
5th row	373450

Common Values

Value	Count	Frequency (%)
347082	7	1.0%
3101295	6	0.8%
CA 2144	6	0.8%
347088	6	0.8%
382652	5	0.7%
S.O.C. 14879	5	0.7%
113760	4	0.6%
1601	4	0.6%
19950	4	0.6%
347077	4	0.6%
Other values (532)	663	92.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
pc	50	5.5%
c.a	26	2.8%
a/5	15	1.6%
2	12	1.3%
ston/o	12	1.3%
347082	7	0.8%
sc/paris	7	0.8%
ca	7	0.8%
soton/o.q	6	0.7%
3101295	6	0.7%
Other values (567)	768	83.8%

Most occurring characters

Value	Count	Frequency (%)
3	569	11.6%
1	569	11.6%
2	472	9.7%
7	407	8.3%
4	381	7.8%
0	340	7.0%
5	324	6.6%
6	315	6.4%
9	259	5.3%
8	234	4.8%
Other values (25)	1015	20.8%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	3870	79.2%
Uppercase Letter	546	11.2%
Other Punctuation	247	5.1%
Space Separator	202	4.1%
Lowercase Letter	20	0.4%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
C	127	23.3%
P	84	15.4%
O	84	15.4%
A	65	11.9%
S	62	11.4%
N	35	6.4%
T	31	5.7%
W	13	2.4%
Q	10	1.8%
I	9	1.6%
Other values (6)	26	4.8%

Decimal Number

Value	Count	Frequency (%)
3	569	14.7%
1	569	14.7%
2	472	12.2%
7	407	10.5%
4	381	9.8%
0	340	8.8%
5	324	8.4%
6	315	8.1%
9	259	6.7%
8	234	6.0%

Lowercase Letter

Value	Count	Frequency (%)
a	5	25.0%
s	5	25.0%
r	4	20.0%
i	4	20.0%
l	1	5.0%
e	1	5.0%

Other Punctuation

Value	Count	Frequency (%)
.	166	67.2%
/	81	32.8%

Space Separator

Value	Count	Frequency (%)
	202	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	4319	88.4%
Latin	566	11.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
C	127	22.4%
P	84	14.8%
O	84	14.8%
A	65	11.5%
S	62	11.0%
N	35	6.2%
T	31	5.5%
W	13	2.3%
Q	10	1.8%
I	9	1.6%
Other values (12)	46	8.1%

Common

Value	Count	Frequency (%)
3	569	13.2%
1	569	13.2%
2	472	10.9%
7	407	9.4%
4	381	8.8%
0	340	7.9%
5	324	7.5%
6	315	7.3%
9	259	6.0%
8	234	5.4%
Other values (3)	449	10.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	4885	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
3	569	11.6%
1	569	11.6%
2	472	9.7%
7	407	8.3%
4	381	7.8%
0	340	7.0%
5	324	6.6%
6	315	6.4%
9	259	5.3%
8	234	4.8%
Other values (25)	1015	20.8%

Fare
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	220
Distinct (%)	30.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	34.69451401

Minimum	0
Maximum	512.3292
Zeros	7
Zeros (%)	1.0%
Negative	0
Negative (%)	0.0%
Memory size	5.7 KiB

Quantile statistics

Minimum	0
5-th percentile	7.225
Q1	8.05
median	15.7417
Q3	33.375
95-th percentile	120
Maximum	512.3292
Range	512.3292
Interquartile range (IQR)	25.325

Descriptive statistics

Standard deviation	52.9189295
Coefficient of variation (CV)	1.52528234
Kurtosis	30.92424901
Mean	34.69451401
Median Absolute Deviation (MAD)	8.2334
Skewness	4.653630368
Sum	24771.883
Variance	2800.4131
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13	41	5.7%
26	30	4.2%
8.05	29	4.1%
10.5	24	3.4%
7.8958	23	3.2%
7.925	18	2.5%
7.75	14	2.0%
7.775	14	2.0%
26.55	13	1.8%
7.8542	13	1.8%
Other values (210)	495	69.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	7	1.0%
4.0125	1	0.1%
5	1	0.1%
6.2375	1	0.1%
6.4375	1	0.1%
6.45	1	0.1%
6.4958	2	0.3%
6.75	2	0.3%
6.975	2	0.3%
7.0458	1	0.1%

Value	Count	Frequency (%)
512.3292	3	0.4%
263	4	0.6%
262.375	2	0.3%
247.5208	2	0.3%
227.525	3	0.4%
211.5	1	0.1%
211.3375	3	0.4%
164.8667	2	0.3%
153.4625	3	0.4%
151.55	4	0.6%

Embark
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	5.7 KiB

2	556
0	130
1	28

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	714
Distinct characters	3
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2
2nd row	0
3rd row	2
4th row	2
5th row	2

Common Values

Value	Count	Frequency (%)
2	556	77.9%
0	130	18.2%
1	28	3.9%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
2	556	77.9%
0	130	18.2%
1	28	3.9%

Most occurring characters

Value	Count	Frequency (%)
2	556	77.9%
0	130	18.2%
1	28	3.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	714	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
2	556	77.9%
0	130	18.2%
1	28	3.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	714	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
2	556	77.9%
0	130	18.2%
1	28	3.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	714	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
2	556	77.9%
0	130	18.2%
1	28	3.9%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	df_index	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Embark
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	2
1	1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38.0	1	0	PC 17599	71.2833	0
2	2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	2
3	3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	2
4	4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	2
5	6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	2
6	7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	2
7	8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	2
8	9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	0
9	10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	2

Last rows

	df_index	PassengerId	Survived	Pclass	Name	Sex	Age	Parch	Ticket	Fare	Embark
704	880	881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	1	230433	26.0000	2
705	881	882	0	3	Markun, Mr. Johann	male	33.0	0	349257	7.8958	2
706	882	883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	7552	10.5167	2
707	883	884	0	2	Banfield, Mr. Frederick James	male	28.0	0	C.A./SOTON 34068	10.5000	2
708	884	885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	SOTON/OQ 392076	7.0500	2
709	885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	5	382652	29.1250	1
710	886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	211536	13.0000	2
711	887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	112053	30.0000	2
712	889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	111369	30.0000	0
713	890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	370376	7.7500	1

Overview

Variables

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Other Punctuation

Space Separator

Open Punctuation

Close Punctuation

Dash Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Uppercase Letter

Decimal Number

Lowercase Letter

Other Punctuation

Space Separator

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot